Cardiac signal processing device

A method for processing cardiac signals comprising the following operations:

• a) receiving input data sets each comprising a plurality of P wave segments each associated with an electrocardiogram track and with an acquisition time window, and a plurality of coronary sinus signals associated with the same acquisition time window and having one or more activation sequence(s),
• b) for a given input data set, for at least some of the P wave segments of the given input data set:
• b1) determining a wave polarity profile type,
• b2) determining at least one extremum feature of a type selected from a group of types comprising the number of positive local extrema, the number of negative local extrema, the positive prominence maximum and the negative prominence maximum, and
• b3) determining at least one integral value of these wave segments P,
• b4) associating the data resulting from the operations b1) to b3) into a group of track P wave data as a function of the electrocardiogram track with which each P wave segment is associated based on which said resultant data have been calculated,
• b5) determining, for each group of track P wave data, a set of track P wave features comprising a polarity profile type, a data set extremum feature value for each calculated extremum feature type and an integral value determined based on the data of the corresponding group of track P wave data,
• b6) determining activation times in at least some of the activation sequences of the coronary sinus signals of the given input data set and to deduce a set of time values therefrom,
• b7) returning a set of data set features comprising on the one hand the set of time values of the operation b6), and, on the other hand, the sets of track P wave features of the operation b5), and
• c) providing a set of data set features obtained in step b7) as input of a machine-learning based locator using decision trees, returning a cardiac region identifier as output.